Machine-learning how to create better AAV gene delivery vehicles

Adeno-associated viruses (AAVs) have become promising vehicles for delivering gene therapies to defective tissues in the human body because they are non-pathogenic and can transfer therapeutic DNA into target cells.

However, while the first gene therapy products approved by the Federal Drug Administration (FDA) use AAV vectors and others are likely to follow, AAV vectors still have not reached their full potential to meet gene therapeutic challenges.

First, currently used AAV capsids are limited in their ability to specifically hone in on the tissue affected by a disease and their wider distribution throughout the human body causes them to be diluted. And secondly, patients' immune systems, after having been exposed to a similar AAV virus, can produce neutralizing antibodies that, even at low levels, can destroy AAVs, blocking the delivery of their therapeutic DNA payloads.

To overcome this neutralization problem, researchers are engineering enhanced AAV capsids they hope to be able to evade the immune system. Currently used methods, including "directed evolution" strategies that fast-track the evolution of a protein in laboratory conditions, only can create a limited diversity of capsids with most of them still resembling the naturally occurring AAV variants.

Team from Wyss Institute for Biologically Inspired Engineering trained multiple machine learning methods and used them to design 200,000 virus variants. 110,689 of these variants produced viable AAV viruses. Between any two naturally occurring AAV serotypes, 12 amino acids within this segment are expected to differ. The team's effort produced more than 57,000 variants that exhibited much higher diversity than this, some containing up to 29 combined substituted or additionally inserted amino acids.